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ServiceM8 MCP Server for CrewAIGive CrewAI instant access to 9 tools to Create Job, Get Company, Get Job, and more

Built by Vinkius GDPR 9 Tools Framework

Connect your CrewAI agents to ServiceM8 through Vinkius, pass the Edge URL in the `mcps` parameter and every ServiceM8 tool is auto-discovered at runtime. No credentials to manage, no infrastructure to maintain.

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The ServiceM8 app connector for CrewAI is a standout in the Industry Titans category — giving your AI agent 9 tools to work with, ready to go from day one.

Vinkius delivers Streamable HTTP and SSE to any MCP client

python
from crewai import Agent, Task, Crew

agent = Agent(
    role="ServiceM8 Specialist",
    goal="Help users interact with ServiceM8 effectively",
    backstory=(
        "You are an expert at leveraging ServiceM8 tools "
        "for automation and data analysis."
    ),
    # Your Vinkius token. get it at cloud.vinkius.com
    mcps=["https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp"],
)

task = Task(
    description=(
        "Explore all available tools in ServiceM8 "
        "and summarize their capabilities."
    ),
    agent=agent,
    expected_output=(
        "A detailed summary of 9 available tools "
        "and what they can do."
    ),
)

crew = Crew(agents=[agent], tasks=[task])
result = crew.kickoff()
print(result)
ServiceM8
Fully ManagedVinkius Servers
60%Token savings
High SecurityEnterprise-grade
IAMAccess control
EU AI ActCompliant
DLPData protection
V8 IsolateSandboxed
Ed25519Audit chain
<40msKill switch
Stream every event to Splunk, Datadog, or your own webhook in real-time

* Every MCP server runs on Vinkius-managed infrastructure inside AWS - a purpose-built runtime with per-request V8 isolates, Ed25519 signed audit chains, and sub-40ms cold starts optimized for native MCP execution. See our infrastructure

About ServiceM8 MCP Server

Connect your ServiceM8 account to any AI agent and take full control of your field service management workflows through natural conversation. ServiceM8 is the ultimate platform for trade and service businesses, and this integration allows you to orchestrate jobs, coordinate staff, and manage client relations without leaving your chat interface.

When paired with CrewAI, ServiceM8 becomes a first-class tool in your multi-agent workflows. Each agent in the crew can call ServiceM8 tools autonomously, one agent queries data, another analyzes results, a third compiles reports, all orchestrated through Vinkius with zero configuration overhead.

What you can do

  • Job & Task Orchestration — List all managed jobs and retrieve detailed metadata, including status and scheduled times programmatically.
  • Staff & Team Coordination — Access and monitor your staff directory to maintain a clear overview of team assignments and availability directly from the AI interface.
  • Client CRM Control — List and search through your client and contact database to keep your professional records synchronized via natural language.
  • Job Activity Tracking — Access real-time activity logs for specific jobs to monitor progress and ensuring high-quality service delivery.
  • Operational Monitoring — Track job categories and manage system metadata to ensure your field service operations are always optimized.

The ServiceM8 MCP Server exposes 9 tools through the Vinkius. Connect it to CrewAI in under two minutes — no API keys to rotate, no infrastructure to provision, no vendor lock-in. Your configuration, your data, your control.

All 9 ServiceM8 tools available for CrewAI

When CrewAI connects to ServiceM8 through Vinkius, your AI agent gets direct access to every tool listed below — spanning job-scheduling, work-orders, field-operations, and more. Every call is secured with network, filesystem, subprocess, and code evaluation entitlements inside a sandboxed runtime. Beyond a simple connection, you get a full AI Gateway with real-time visibility into agent activity, enterprise governance, and optimized token usage.

create_job

Requires description and company_uuid. Create a new ServiceM8 job

get_company

Get details for a specific company

get_job

Get details for a specific job

list_categories

List job categories

list_companies

List all companies (clients)

list_contacts

List all company contacts

list_job_activity

Get activity history for a job

list_jobs

List all ServiceM8 jobs

list_staff

List ServiceM8 staff members

Connect ServiceM8 to CrewAI via MCP

Follow these steps to wire ServiceM8 into CrewAI. The entire setup takes under two minutes — your credentials stay safe behind the Vinkius.

01

Install CrewAI

Run pip install crewai
02

Replace the token

Replace [YOUR_TOKEN_HERE] with your Vinkius token from cloud.vinkius.com
03

Customize the agent

Adjust the role, goal, and backstory to fit your use case
04

Run the crew

Run python crew.py. CrewAI auto-discovers 9 tools from ServiceM8

Why Use CrewAI with the ServiceM8 MCP Server

CrewAI Multi-Agent Orchestration Framework provides unique advantages when paired with ServiceM8 through the Model Context Protocol.

01

Multi-agent collaboration lets you decompose complex workflows into specialized roles, one agent researches, another analyzes, a third generates reports, each with access to MCP tools

02

CrewAI's native MCP integration requires zero adapter code: pass Vinkius Edge URL directly in the `mcps` parameter and agents auto-discover every available tool at runtime

03

Built-in task delegation and shared memory mean agents can pass context between steps without manual state management, enabling multi-hop reasoning across tool calls

04

Sequential and hierarchical crew patterns map naturally to real-world workflows: enumerate subdomains → analyze DNS history → check WHOIS records → compile findings into actionable reports

ServiceM8 + CrewAI Use Cases

Practical scenarios where CrewAI combined with the ServiceM8 MCP Server delivers measurable value.

01

Automated multi-step research: a reconnaissance agent queries ServiceM8 for raw data, then a second analyst agent cross-references findings and flags anomalies. all without human handoff

02

Scheduled intelligence reports: set up a crew that periodically queries ServiceM8, analyzes trends over time, and generates executive briefings in markdown or PDF format

03

Multi-source enrichment pipelines: chain ServiceM8 tools with other MCP servers in the same crew, letting agents correlate data across multiple providers in a single workflow

04

Compliance and audit automation: a compliance agent queries ServiceM8 against predefined policy rules, generates deviation reports, and routes findings to the appropriate team

Example Prompts for ServiceM8 in CrewAI

Ready-to-use prompts you can give your CrewAI agent to start working with ServiceM8 immediately.

01

"List all our active jobs on ServiceM8."

02

"Show me all staff members in the organization."

03

"Fetch the activity history for job UUID 550e8400-e29b-41d4-a716-446655440000."

Troubleshooting ServiceM8 MCP Server with CrewAI

Common issues when connecting ServiceM8 to CrewAI through the Vinkius, and how to resolve them.

01

MCP tools not discovered

Ensure the Edge URL is correct. CrewAI connects lazily when the crew starts. check console output.
02

Agent not using tools

Make the task description specific. Instead of "do something", say "Use the available tools to list contacts".
03

Timeout errors

CrewAI has a 10s connection timeout by default. Ensure your network can reach the Edge URL.
04

Rate limiting or 429 errors

Vinkius enforces per-token rate limits. Check your subscription tier and request quota in the dashboard. Upgrade if you need higher throughput.

ServiceM8 + CrewAI FAQ

Common questions about integrating ServiceM8 MCP Server with CrewAI.

01

How does CrewAI discover and connect to MCP tools?

CrewAI connects to MCP servers lazily. when the crew starts, each agent resolves its MCP URLs and fetches the tool catalog via the standard tools/list method. This means tools are always fresh and reflect the server's current capabilities. No tool schemas need to be hardcoded.
02

Can different agents in the same crew use different MCP servers?

Yes. Each agent has its own mcps list, so you can assign specific servers to specific roles. For example, a reconnaissance agent might use a domain intelligence server while an analysis agent uses a vulnerability database server.
03

What happens when an MCP tool call fails during a crew run?

CrewAI wraps tool failures as context for the agent. The LLM receives the error message and can decide to retry with different parameters, fall back to a different tool, or mark the task as partially complete. This resilience is critical for production workflows.
04

Can CrewAI agents call multiple MCP tools in parallel?

CrewAI agents execute tool calls sequentially within a single reasoning step. However, you can run multiple agents in parallel using process=Process.parallel, each calling different MCP tools concurrently. This is ideal for workflows where separate data sources need to be queried simultaneously.
05

Can I run CrewAI crews on a schedule (cron)?

Yes. CrewAI crews are standard Python scripts, so you can invoke them via cron, Airflow, Celery, or any task scheduler. The crew.kickoff() method runs synchronously by default, making it straightforward to integrate into existing pipelines.